Sex differences in the etiology and burden of heart failure across country income level: analysis of 204 countries and territories 1990–2019
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a global epidemic. OBJECTIVE: To assess global sex differences in HF epidemiology across country income levels. METHODS AND RESULTS: Using Global Burden of Disease (GBD) data from 204 countries and territories 1990-2019, we assessed sex differences in HF prevalence, etiology, morbidity, and temporal trends across country sociodemographic index or gross national income. We derived age-standardized rates. Of 56.2 million (95% uncertainty interval [UI] 46.4-67.8 million) people with HF in 2019, 50.3% were females and 69.2% lived in low- and middle-income countries; age-standardized prevalence was greater in males and in high-income countries. Ischaemic and hypertensive heart disease were top causes of HF in males and females, respectively. There were 5.1 million (95% UI 3.3-7.3 million) years lived with disability, distributed equally between sexes. Between 1990 and 2019, there was an increase in HF cases, but a decrease in age-standardized rates per 100 000 in males (9.1%, from 864.2 to 785.7) and females (5.8%, from 686.0 to 646.1). High-income regions experienced a 16.0% decrease in age-standardized rates (from 877.5 to 736.8), while low-income regions experienced a 3.9% increase (from 612.1 to 636.0), largely consistent across sexes. There was a temporal increase in age-standardized HF from hypertensive, rheumatic, and calcific aortic valvular heart disease, and a decrease from ischaemic heart disease, with regional and sex differences. CONCLUSION: Age-standardized HF rates have decreased over time, with larger decreases in males than females; and with large decreases in high-income and small increases in low-income regions. Sex and regional differences offer targets for intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".